150 Psychologie
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Background: Standardized neuropsychological testing serves to quantify cognitive impairment in multiple sclerosis (MS) patients. However, the exact mechanism underlying the translation of cognitive dysfunction into difficulties in everyday tasks has remained unclear. To answer this question, we tested if MS patients with intact vs. impaired information processing speed measured by the Symbol Digit Modalities Test (SDMT) differ in their visual search behavior during ecologically valid tasks reflecting everyday activities.
Methods: Forty-three patients with relapsing-remitting MS enrolled in an eye-tracking experiment consisting of a visual search task with naturalistic images. Patients were grouped into “impaired” and “unimpaired” according to their SDMT performance. Reaction time, accuracy and eye-tracking parameters were measured.
Results: The groups did not differ regarding age, gender, and visual acuity. Patients with impaired SDMT (cut-off SDMT-z-score < −1.5) performance needed more time to find and fixate the target (q = 0.006). They spent less time fixating the target (q = 0.042). Impaired patients had slower reaction times and were less accurate (both q = 0.0495) even after controlling for patients' upper extremity function. Exploratory analysis revealed that unimpaired patients had higher accuracy than impaired patients particularly when the announced target was in unexpected location (p = 0.037). Correlational analysis suggested that SDMT performance is inversely linked to the time to first fixation of the target only if the announced target was in its expected location (r = −0.498, p = 0.003 vs. r = −0.212, p = 0.229).
Conclusion: Dysfunctional visual search behavior may be one of the mechanisms translating cognitive deficits into difficulties in everyday tasks in MS patients. Our results suggest that cognitively impaired patients search their visual environment less efficiently and this is particularly evident when top-down processes have to be employed.
Ergodic subspace analysis
(2020)
Properties of psychological variables at the mean or variance level can differ between persons and within persons across multiple time points. For example, cross-sectional findings between persons of different ages do not necessarily reflect the development of a single person over time. Recently, there has been an increased interest in the difference between covariance structures, expressed by covariance matrices, that evolve between persons and within a single person over multiple time points. If these structures are identical at the population level, the structure is called ergodic. However, recent data confirms that ergodicity is not generally given, particularly not for cognitive variables. For example, the <i>g</i> factor that is dominant for cognitive abilities between persons seems to explain far less variance when concentrating on a single person’s data. However, other subdimensions of cognitive abilities seem to appear both between and within persons; that is, there seems to be a lower-dimensional subspace of cognitive abilities in which cognitive abilities are in fact ergodic. In this article, we present ergodic subspace analysis (ESA), a mathematical method to identify, for a given set of variables, which subspace is most important within persons, which is most important between person, and which is ergodic. Similar to the common spatial patterns method, the ESA method first whitens a joint distribution from both the between and the within variance structure and then performs a principle component analysis (PCA) on the between distribution, which then automatically acts as an inverse PCA on the within distribution. The difference of the eigenvalues allows a separation of the rotated dimensions into the three subspaces corresponding to within, between, and ergodic substructures. We apply the method to simulated data and to data from the COGITO study to exemplify its usage.
Magnitude processing is one of the most central cognitive mechanisms that underlie persistent mathematics difficulties. No consensus has yet been reached about whether these difficulties can be predominantly attributed to deficits in symbolic or nonsymbolic magnitude processing. To investigate this issue, we assessed symbolic and nonsymbolic magnitude representations in children with low or typical achievement in school mathematics. Response latencies and the distance effect were comparable between groups in both symbolic and nonsymbolic tasks. The results indicated that both typical and low achievers were able to access magnitude representation via symbolic and nonsymbolic processing. However, low achievers presented higher error rates than typical achievers, especially in the nonsymbolic task. Furthermore, measures of nonsymbolic magnitude explained individual differences in school mathematics better than measures of symbolic magnitude when considering all of the children together. When examining the groups separately, symbolic magnitude representation explained differences in school mathematics in low achievers but not in typical achievers. These results suggest that symbolic magnitude is more relevant to solving arithmetic problems when mathematics achievement is particularly low. In contrast, individual differences in nonsymbolic processing appear to be related to mathematics achievement in a more general manner.